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Blip.tv changed its terms of service in November 2013and removed videos of conferences and tech talks. CarlFK's blip account had videos of tech talks in the Chicago area.This is an archive of his account
Topic: programming
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**Machine Learning** is about *using the right features, to build the right models, to achieve the right tasks* [[Flach, 2012]][0] However, to come up with a definition of what actually means **right** for the problem at the hand, it is required to analyse huge amounts of data, and to evaluate the performance of different algorithms on these data. However, deriving a working machine learning solution for a given problem is far from being a *waterfall* process. It is an iterative process where...
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Introduction =========== This talk will first introduce the audience to WebRTC and then discuss about how to implement the server side logic of a WebRTC app using Python. WebRTC is a free, open project that enables web browsers with plugin-less Real-Time Communications (RTC) capabilities via simple JavaScript APIs. What makes WebRTC special is that the data travels from one client to another without going through the server. The main functions of WebRTC can be broadly categorized into three...
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Who am I ? ========= * a programmer * a high school student * a blogger * Pythonista * and tea lover - Creator of freepythontips.wordpress.com - I made soundcloud-dl.appspot.com - I am a main contributor of youtube-dl. - I teach programming at my school to my friends. - It's my first programming related conference. - The life of a python programmer in Pakistan What this talk is about ? ================== - What is Web Scraping and its usefulness - Which libraries are available for the job -...
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Python is a great language for writing command-line tools - which is why so much of Linux is secretly written in Python these days. Unfortunately, what starts as a simple script can quickly get out of hand as more features are added and more people start using it! The talk will consist of a tour through various useful libraries and practical code showing how each can be used, and include advice on how to best structure simple and complex command-line tools. Things to consider when writing...
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Python has been used in educational programmes ever since. With a bandwidth that large, navigating the landscape of Python tutorials is hard indeed. This talk will look at successful Python teaching material. From the numerous iterations our material has gone through, we draw conclusions on what's crucial in teaching Python. It will introduce how the OpenTechSchool is teaching Python and what measures it found most effective in spreading programming in general and Python in particular. Among...
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*(Special note: [Pythonista][1] and [Editorial][2] are designed and created **in Berlin**).* 1. Definition and history of Pythonista 2. Constraints that Apple's App Store imposes on development environments 3. Pythonista User Experience and [Community Forum][3] 4. [iOS-specific modules][4]: contacts, Dropbox, location, notification, photo, scene, sound, speech 5. Source code demonstrations * GPS: Combine location and requests to deliver local weather in real-time * Image: Use photo and PIL to...
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The best filter algorithm to fuse multiple sensor informations is the Kalman filter. To implement it for non-linear dynamic models (e.g. a car), analytic calculations for the matrices are necessary. In this talk, one can see, how the IPython Notebook and Sympy helps to develop an optimal filter to fuse sensor information from different sources (e.g. acceleration, speed and GPS position) to get an optimal estimate. more: http://balzer82.github.io/Kalman/
Topics: python, pydata, pydataberlin2014
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Kafka is a distributed, persistent, replicated publish-subscribe system which can be used for variety of use cases like activity stream processing, log collection etc. The design philosophy of Kafka is such that it enables real-time processing. Storm is a high-volume, continuous, reliable stream processing system developed at BackType and open-sourced by Twitter. Storm is being widely used in lot of organizations and has variety of uses-cases like: * Realtime analytics * Distributed RPC * ETL...
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We present the Shogun Machine Learning Toolbox, a unified framework for Machine Learning algorithms. Machine Learning (ML) is the art of finding structure in data in an automated way and has given rise to a wide range of applications such as recommendation systems, object recognition, brain-computer interfaces, robotics, predicting stock prices, etc. Our toolbox offers extensive bindings with other software and computing languages, Python being the major target. The library was initiated in...
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The Python DB-API 2.0 provides a direct interface to many popular database backends. It makes interaction with relational database very straight forward and allows tapping into the full set of features these databases provide. The talk will cover advanced database topics which are relevant in production environments such as locks, distributed transactions and transaction isolation. ---- The talk will give an in-depth discussion of advanced database programming topics based on the Python DB-API...
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How we created a scalable mass VM hosting for open source web apps with python, LXC and linux with a web-UI based on django and angularjs. We'll show the underlying architecture of this service, several linux internals that make this possible and we'll talk about bitter failure during development. This talk will be python- and linux-centric with some hints for integrating angularjs into django.
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The Cython compiler is the most widely used static compiler for Python. The code it generates is used in countless critical applications that process huge amounts of data world wide. Cython has two major use cases: to compile Python code into fast native extension modules, and to connect native code to the CPython runtime. The main goal of the Cython project is to make it easy for users to manually optimise their Python code to make it run at C speed. This talk by one of the core developers...
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A new class of cheap consumer EEG devices allows ordinary hackers and even high school students a glimpse into the human brain. This talk will present how to use the "Neurosky Mindwave" headset with python software, and lay out the basic scientific and technical background. The Mindwave Mobile is a device that can be easily talked to using bluetooth, and it talks a binary protocol which is specifically designed to be useful without much computing power in the receiving device or...
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Experiences from building a recommendation engine for patent search using pythonic NLP and topic modeling tools such as Gensim.
Topics: python, pydata, pydataberlin2014
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For data, and data science, to be the fuel of the 21th century, data driven applications should not be confined to dashboards and static analyses. Instead they should be the driver of the organizations that own or generates the data. Most of these applications are web-based and require real-time access to the data. However, many Big Data analyses and tools are inherently batch-driven and not well suited for real-time and performance-critical connections with applications. Trade-offs become...
Topics: python, pydata, pydataberlin2014
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Probabilistic Programming allows flexible specification of statistical models to gain insight from data. Estimation of best fitting parameter values, as well as uncertainty in these estimations, can be automated by sampling algorithms like Markov chain Monte Carlo (MCMC). The high interpretability and flexibility of this approach has lead to a huge paradigm shift in scientific fields ranging from Cognitive Science to Data Science and Quantitative Finance. PyMC3 is a new Python module that...
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Scikit-Learn (http://scikit-learn.org/) is a well-known and popular framework for machine learning that is used by Data Scientists all over the world. We show in a practical way how you can add your own estimator following the interfaces of Scikit-Learn. First we give a small introduction to the design of Scikit-Learn and its inner workings. Then we show how easily Scikit-Learn can be extended by creating an own estimator. In order to demonstrate this, we extend Scikit-Learn by the popular and...
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This talk will give a basic introduction to the pySPACE framework and its current applications. pySPACE (Signal Processing And Classification Environment) is a modular software for the processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial...
Topics: python, pydata, pydataberlin2014
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When talking of parallel processing, some task requires a substantial set-up time. This is the case of Natural Language Processing (NLP) tasks such as classification, where models need to be loaded into memory. In these situations, we can not start a new process for every data set to be handled, but the system needs to be ready to process new incoming data. This talk will look at job queue systems, with particular focus on gearman. We will see how we are using it at Synthesio for NLP tasks; how...
Topics: python, pydata, pydataberlin2014
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This talk distills information from the open source guide [Full Stack Python](http://www.fullstackpython.com/) I wrote into a 30 minute talk on web stack layers. An approximate timeline for this talk would be: * 5 min: intro story * 5 min: what the web developers need to know about virtual servers, web servers, and WSGI servers * 5 min: what do web frameworks provide? * 5 min: what are the most important parts of your web application to analyze and monitor? * 5 min: static files and execution...
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Can empirical samples unveil the big picture? Is chess games descriptions expose good enough data to gain understanding of chess rules - legal piece moves, castling, check versus checkmate, etc. Which features are important in describing a chess game and which features are not. What is a good representation of a chess game for this uses. What is the minimal sample size which is required in order to learn this in a good enough manner and where this learning can go wrong. **Ne3 => E=mc2**...
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Infrastructure/Scaling is a topic really close to me, I'd like to have the chance to talk about how we set this up in the company I work for. Our infrastructure is around 10-15 servers, provisioned on different cloud providers, so a good size infrastructure. Presentation is going to be divided in 3 parts, first part is going to be focused on comparing sysadmin and devops, then there will be an introduction to the basic concepts of Ansible. I want to spend most on the last part, which is going...
